Exposure scenarios in dairy cattle

Regarding the basic experimental design, the studies can be classified into field, incident, controlled feeding and mass balance studies. In field studies, in a stable or on pasture, the daily intake of contaminants could be assumed as being steady, but especially for grazing animals, this assumption is uncertain. Incidents and case reports refer to short-term contamination events where the origin and extent of the exposure may be uncertain.

In controlled feeding studies, the daily amount of contaminant uptake is known; contaminants are either spiked into the daily ration or they are already present in known amounts, with the total feed intake recorded. Alternatively, they are applied as a bolus (e.g. gelatine capsule) into the rumen. The time of exposure in feeding studies varied from 1 d^{(Reference Slob, Olling and Derks62,Reference Gardner, Righter and Roach75,Reference Jones, Safe and Morcom79,Reference Jones, Safe and Morcom80,Reference Heeschen and Blüthgen87,Reference Ewers88,Reference Platonow, Funnell and Bullock95,Reference Platonow and Meads97,Reference Arnott, Bullock and Platonow98,Reference Olling, Derks and Berende102,Reference Olling, Berende and Derks103,Reference Safe, Platonow and Hutzinger120,Reference Sips, van Eijkeren and Bode124–Reference Ruoff and Fouzy126)} to several weeks (<28 d)^{(Reference Steimer, Blüthgen and Heeschen60,Reference Jensen and Hummel77,Reference Willett and Liu78,Reference Arnott, Bullock and Platonow98,Reference McLachlan and Richter113)} (Table 2). In some feeding studies, the animals were exposed to PCDD/Fs and PCBs during their dry period^{(Reference Roos, Berende and Traag61,Reference Ewers88,Reference Ruoff91,Reference Schulz93,Reference Schulz, Wiesmüller and Appuhn94,Reference Klein, Drochner and Forschner100,Reference Driesen, Lerch and Siegenthaler114,Reference Perry, Everson and Hendrix115,Reference Perry, Everson and Hendrix119)} . Since elimination via lactation is not possible in this case, PCDD/Fs and PCBs accumulate in the gravid cows’ body fat – and likely in the unborn foetus^{(Reference Hirako127)}. Mass balance studies are a subcategory of feeding studies in which feed of a known contaminant concentration (background or higher) is given over a prolonged time of several weeks, with the intention of approaching a steady state between contaminant intake and elimination through milk and other excretion pathways^{(Reference Thomas, Sweetman and Jones65–Reference Winters, Fries and Lorber68,Reference Fries, Paustenbach and Mather72,Reference Fries, Paustenbach and Luksemburg73,Reference McLachlan, Thoma and Reissinger109–Reference McLachlan111,Reference McLachlan and Richter113,Reference Driesen, Lerch and Siegenthaler114,Reference Fries128)} . Furthermore, the transfer of contaminants into milk has been studied after intravenous^{(Reference Safe, Platonow and Hutzinger120)} and intramuscular^{(Reference Vrecl, Ursic and Pogacnik125)} applications. The dose of PCDD/Fs and PCBs administered in feeding studies has decreased since the 1970s, likely driven by improvements in analytical detection capabilities (e.g. limit of quantification).

Table 2. Studies used in assessment of transfer rates (TR), transfer factors (TF) and biotransfer factors (BTF) from feed to cow milk in chronological order

* Converted from weeks to days by multiplying by 7.

Finally, at least four studies were available^{(Reference Travis and Hattemer-Frey136–Reference Webster and Connett139)} where a theoretical approach was used to assess the exposure and transfer of PCDD/Fs and PCBs in dairy cattle. Although different exposure scenarios in the available studies provide limited comparability of data, all these experimental approaches have their specific advantages to gain insight into the fate of PCDD/F and PCB congeners in dairy cattle and to develop predictive models.

Cattle breeds and other ruminants

Studies that presented feed to milk transfer parameters were based on various lactating dairy cows and beef cattle. Specific cattle breeds were named in some studies, but not in all (Table 1). On the basis of their specific genetic potential, the milk yield differs between cattle breeds. Owing to breeding measures over the past decades, these performance differences have become more evident for specialised dairy cow breeds like Holstein cattle^{(Reference VandeHaar and St-Pierre140)}. The potential influences of several breed-associated performance and metabolic parameters on the transfer of xenobiotics in milk of ruminants have already been shown in a modelling approach^{(Reference MacLachlan141)}. The authors of this study found that the elimination half-lives of xenobiotics can decrease significantly with increasing milk yield and increasing milk fat content, which was particularly evident for substances with slow liver metabolic rates. But, to date, no systematic study has been carried out to evaluate a potential influence of dairy cattle breed on PCDD/F and PCB transfer.

The extrapolation of the data from previous studies with lower milk-yield breeds to high-performance dairy cows needs to be addressed in the future and should be adopted in toxicokinetic models. In addition, the transferability of data might be further limited by other factors such as metabolic state, body weight and body fat content as well as milk and milk fat production, and will be discussed below.

In addition to cattle, studies have also been carried out with other ruminants, including dairy sheep^{(Reference Vrecl, Ursic and Pogacnik125)}, dairy goats^{(Reference Safe, Platonow and Hutzinger120,Reference Sips, van Eijkeren and Bode124,Reference Ruoff and Fouzy126,Reference Arstila, Reggiani and Sorvari142–Reference Fournier, Rychen and Marchand148)} and dairy buffaloes^{(Reference Diletti, Ceci and Ippoliti149)}. While the milk fat content of goats is very similar to that of cattle, it is more than twice as high in the milk of sheep and buffalo^{(Reference Park and Haenlein150)}. Regarding the lipophilic properties of PCDD/Fs and PCBs, these different milk fat contents could influence the transfer rates from feed to milk. Even though there are currently no experimental data available on this aspect, the composition of milk should be considered comparing transfer data in different ruminant species. Furthermore, Rychen et al. (2008) hypothesised goats to be a valuable ‘lactating animal model’ with a transfer behaviour of PCDD/Fs into milk similar to cows^{(Reference Rychen, Jurjanz and Toussaint52)}. Therefore, the aforementioned studies on goats will be referenced in the following if appropriate.

Data integrity

A crucial point in evaluating and comparing transfer studies is how and what data were published, as well as how to handle ambiguous or missing data. This is especially important when essential data for transfer modelling, such as milk and milk fat yields, are not reported. Furthermore, data expression in a variety of units and dimensions makes harmonisation challenging. Most studies expressed the amount of PCDD/F and PCB on the basis of milk fat corresponding to regulatory maximum residue levels or daily excretion via milk. However, in some studies the amount was expressed on a whole milk basis^{(Reference Jensen and Hummel77,Reference Platonow, Funnell and Bullock95,Reference Arnott, Bullock and Platonow98,Reference Perry, Everson and Hendrix115,Reference Arstila, Reggiani and Sorvari142,Reference Willett151)} , not necessarily providing information about the respective milk fat content.

Another important aspect of data integrity is the length of the exposure period. During the initial first weeks of exposure, contaminant levels in milk fat increase rapidly, while after weeks of exposure, the increase flattens to slowly approach a steady state. Transfer parameters calculated at the end of a short exposure period will lead to an underestimation compared with steady-state or near-steady-state values. With a constant intake of PCDD/Fs and PCBs, a near steady state was postulated in lactating cows after 17 and 21 d for PCDDF and PCB TEQs^{(Reference Huwe and Smith82,Reference Huwe and Smith135)} , 40–60 d for Aroclor 1254^{(Reference Fries70)} and for specific congeners after 14–42 d^{(Reference Lorenzi, Angelone and Ferretti86)}, 21–28 d^{(Reference Ewers and Heeschen89,Reference Ewers, Heeschen and Reichmuth131)} , 28 d^{(Reference Fries, Paustenbach and Mather72,Reference Fries, Paustenbach and Luksemburg73)} , 33 d^{(Reference Hoogenboom, Traag and Fernandes13)} and 40–70 d^{(Reference Blüthgen, Heeschen and Ruoff134)}. Predictive modelling suggested it would take approximately 150–200 d to reach a real steady state^{(Reference Hoogenboom, Traag and Fernandes13,Reference Hoogenboom, Zeilmaker and van Eijkeren42)} . However, this might not be reachable owing to metabolic changes during lactation^{(Reference MacLachlan and Bhula15,Reference Brambilla, Fochi and Falce41)} . Therefore, an exposure period of at least 28 d is suggested for transfer studies of lipophilic persistent organic pollutants^{(Reference MacLachlan and Bhula15,Reference MacLachlan141)} . The time to reach steady state for PCDD/Fs and PCBs was also calculated for dairy buffaloes^{(Reference Diletti, Ceci and Ippoliti149)} and goats^{(Reference Costera, Feidt and Marchand144,Reference Ounnas, Feidt and Toussaint146,Reference Fournier, Rychen and Marchand148)} .

Owing to limited analytical performance and limited toxicological assessments, early studies did not differentiate between single congeners. Instead, commercially available PCB-mixtures, such as Aroclor 1254, were often used as a reference for the quantification of PCBs, and results were expressed as Aroclor 1254 equivalents^{(Reference Fries, Marrow and Gordon71,Reference Gardner, Righter and Roach75,Reference Willett and Liu78,Reference Willett, Liu and Durst81,Reference Platonow, Funnell and Bullock95,Reference Saschenbrecker, Funnell and Platonow96,Reference Arnott, Bullock and Platonow98,Reference Perry, Everson and Hendrix115,Reference Perry, Everson and Hendrix119,Reference Willett151)} . Furthermore, some of the aforementioned studies with Aroclor 1254 were later re-evaluated to adjust for different approaches in quantification^{(Reference Willett, Liu and Fries121)}. In earlier studies, another analytical challenge could have been the inefficient chromatographic performance of the available gas chromatography columns to fully separate isomers^{(Reference McLachlan, Thoma and Reissinger109,Reference McLachlan110)} .

In literature, data for single congeners are sometimes omitted in favour of the sum of the parameters. The WHO₂₀₀₅ TEQ^{(Reference Van den Berg, Birnbaum and Denison26)} describes our current understanding of the relative toxicity of the congeners and has its justification in risk analysis, being currently used for action and maximum levels in feed and food. Nevertheless, any future changes in the TEF for a single congener will practically invalidate entire study outcomes for which the congener-specific data are not available. Furthermore, as transfer parameters are congener specific with a wide range of variation, transfer data derived for mixtures like Aroclor 1254, or based on sum-TEQ values, are restricted to the respective congener profiles present in the contamination source. Additionally, owing to the congener-specific transfer parameters, the congener profile in milk fat is not identical to that of the source. For these reasons, future publications are advised to report congener-specific data.

Another critical point in assessing previous studies is that transfer parameters are sometimes depicted graphically but not necessarily in text or tables^{(Reference Fries, Paustenbach and Mather72,Reference McLachlan and Richter113,Reference Fürst, Krause and Hein122,Reference Costera, Feidt and Marchand144)} . However, graphical depictions of milk fat concentrations of PCDD/Fs and PCBs during exposure and depuration could be used retrospectively to derive transfer parameters if these were not given in papers, for example Refs.^{(Reference Slob, Olling and Derks62,Reference Fries70,Reference Fries, Marrow and Gordon71,Reference Furr, Mertens and Gutenmann74,Reference Gardner, Righter and Roach75,Reference Saschenbrecker, Funnell and Platonow96,Reference Arnott, Bullock and Platonow98,Reference Traag, Mengelers and Kan105,Reference Arstila, Reggiani and Sorvari142)} . For this review, we extracted data from plots using tools like the WebPlotDigitizer^{(Reference Rohatgi152)}, which adds another level of uncertainty. While it is certainly possible to calculate transfer parameters from the tabulated data given in some studies^{(Reference Piskorska-Pliszczynska, Maszewski and Mikolajczyk43,Reference Jones, Safe and Morcom79,Reference Ewers88,Reference Ruoff91,Reference Platonow, Funnell and Bullock95,Reference Derks, Berende and Olling153)} , we saw those as derivative calculations and thus beyond the scope of this review. Aside from milk fat concentrations, some studies include PCDD/F and PCB concentrations in blood, faeces, body fat compartments or organs, enabling a better understanding of the kinetics, which can also be exploited with toxicokinetic modelling. On the basis of these limitations, it is clear that comparison of data from different studies is not trivial.

Transfer rates

Transfer rates (TR), historically called carry-over rates or carry-over ratio, describe the fraction of congener intake with the diet (mass or mole) that is excreted with the milk. While TRs can be calculated for any given time period during an experiment or an incident, they reach a maximum when a steady state between constant intake and output is reached. The TR is used to assess the balances of the mass flow.

(1) $${\it{TR}}\left[ {\rm{\% }} \right] = {{{\it{DailyExcretionViaMilk}}\left[ {{{{\it{ng}}} \over {\it{d}}}} \right]} \over {{\it{DailyIntakeViaFeed}}\left[ {{{{\it{ng}}} \over {\it{d}}}} \right]}} \times 100{\rm{\% }}$$

Re-arranging this formula enables prediction of the daily excretion via milk for a given TR and a known daily intake via feed, that is,

(2) $$DailyExcretionViaMilk\left[ {{{ng} \over d}} \right] = TR\left[ \% \right]/100\% \times DailyIntakeViaFeed\left[ {{{ng} \over d}} \right]$$

This prediction from the re-arrangement of equation (1) can be seen as the simplest kind of model^{(Reference Numata, Schafft and Spolders154)} and is only valid if the steady state has been reached and the conditions of the prediction are the same as in the determination of TR. TR is a ratio of amounts of contaminant, and is thus insensitive to the basis (e.g. dry matter for feed or milk fat for milk). Most studies expressed the transfer of contaminants into milk as TRs, and thus for all PCDD/F and PCB congeners TRs are available, as seen in the boxplot of Fig. 1. The statistical values behind this boxplot can be found in Supplementary Materials Part A, Table S1. The data used for this analysis are derived from the transfer studies listed in Table 2. Owing to differences in the reported transfer rates for the same feeding study, only the more recent parameters for PCDD/Fs and PCBs by McLachlan^{(Reference McLachlan, Thoma and Reissinger109–Reference McLachlan111)} were used for data analysis. We have also included transfer rates with a negative sign, such as those reported by Brambilla et al. (2008)^{(Reference Brambilla, Fochi and Falce41)} and Lorenzi et al. (2020)^{(Reference Lorenzi, Angelone and Ferretti86)}. Although physically meaningless, a negative transfer rate can result from a background exposure correction containing measurement and other errors. Thomas et al. (1999) similarly reported a transfer rate above 100% for PCB-118, which will be discussed at a later point in this review^{(Reference Thomas, Sweetman and Jones66)}.

Fig. 1. Boxplots of transfer rates (TRs) for cows. Scatter points represent available data points from selected literature (refer to Table 2). Boxes (N > 5) are defined as the interquartile range (IQR) between 25th percentile (Q1) and 75th percentile (Q3) of the data according to the standard method. The black line in the box represents the median. Whiskers include data within 1.5 times of IQR below Q1 and above Q3. Plot generated with Python 3.10 using the Seaborn, Matplotlib, Numpy and Pandas libraries.

According to this analysis, TRs of highly chlorinated ndl-PCBs and dl-PCBs are, with some exceptions, higher compared with PCDD/Fs. Altogether, congener-specific TRs lie in the range between <1% and 100%. Despite the consultation of data from comparable studies (Table 2), coefficients of variation (CV) were between 29% and 204% (Table S1), with a trend of decreasing CV at higher TRs. The wide range of calculated TRs (Fig. 1) can be due to several reasons. For instance, the data of certain studies were of low accuracy, owing to low analytical performance at lower TRs; important experimental parameters were not considered, such as contaminant source-dependent bioavailability; or the inter-individual variance between animals was very high for unknown reasons. Moreover, for several congeners, only a very limited number of studies investigating a small number of animals exist, which are thus error prone. In any case, for the purpose of modelling, the large variability in the data negatively affects the predictive power.

Transfer factors

For regulatory purposes and risk assessment, the transfer factor (TF), also known as bioconcentration factor (BCF), carry-over factor (CoF) or accumulation factor (AF), has played a key role until now. This dimensionless factor describes the quotient of the congener concentration in milk (fat) and its concentration in the feed.

(3) $$TF = {{{\it{ConcentratonInMilkFat}}\left[ {{{{\it{ng}}} \over {{\it{kg}}}}} \right]} \over {{\it{ConcentrationInFeed}}\left[ {{{{\it{ng}}} \over {{\it{kg}}}}} \right]}}{\rm{\;}}$$

TF is a ratio of concentrations of a contaminant, and is thus dependent on the basis used. The standard basis from animal nutrition is compound feed with 88% dry matter for feed and milk fat for milk, as used in regulatory standards. The numerical value of TF will vary if other bases are used (e.g. wet weight or just mineral components of compound feed). TF has the disadvantage of having a strong dependence on, for example, milk fat yield. Furthermore, the concept of TF can be applied to describe the transfer and storage of contaminants into fat tissue. This formula can also be re-arranged so that it can be used to predict the concentration in milk fat given concentration in feed and the TF, that is,

(4) $$ConcentrationInMilkfat\left[ {{{ng} \over g}} \right] = TF\; \times \;ConcentrationInFeed\left[ {{{ng} \over g}} \right]$$

TR and TF are related through the feed efficiency (FE), or the ratio of milk fat produced per kg of feed (88% dry matter), such that:

(5) \begin{align}{\it{TR}}\left[ {\rm{\% }} \right] &= {\it{TF}} \times {\it{FE}}\\ &= {{{\it{ConcentrationInMilkfat}}\left[ {{{{\it{ng}}} \over {{\it{kg}}}}} \right]} \over {{\it{ConcentrationInFeed}}\left[ {{{{\it{ng}}} \over {{\it{kg}}}}} \right]}} \times {{{\it{milkfat}}\left[ {{{{\it{kg}}} \over {\it{d}}}} \right]} \over {{\it{feed}}\left[ {{{{\it{kg}}} \over {\it{d}}}} \right]}}\\ &\quad \times 100\% \end{align}

While the conversion between TR and TF is given by equation (5), the data in each study do not always allow for it to be effectively used. In this review, we have opted for only reproducing published values. TFs are less well documented than TRs. For example, there are no data given for some dl-PCBs, and for the lower chlorinated ndl-PCBs there is only one publication by Ewers (1987)^{(Reference Ewers88)} that calculated TFs on the basis of a study by Tuinstra et al. (1981)^{(Reference Tuinstra, Vreman and Roos57)}. While Tuinstra et al. (1981) published enough data to calculate TFs, no data were given for PCB-28. It is therefore unknown how Ewers (1987) calculated the TF value, and thus this value was omitted. Furthermore, Connett and Webster (1987)^{(Reference Connett and Webster58)}, McLachlan et al. (1990)^{(Reference McLachlan, Thoma and Reissinger109)} and Huwe and Smith (2003)^{(Reference Huwe and Smith82)} calculated TFs on the basis of wet weight feed intake, which is not comparable to dry weight intake. Hence, these TFs were not used for further data analysis.

TFs based on studies listed in Table 2 are depicted in Supplementary Materials Part A, Table S2 and plotted in Fig. 2. The coefficient of variation of congener-specific TFs lies between 15% and 154%, again with a trend of lower CV for congeners with higher TFs. The reason for the wide range of calculated TFs (Fig. 2) may be similar to those presented above for TRs.

Fig. 2. Boxplots of transfer factors (TFs) in 88% dry matter basis for the feed and milk fat basis for the milk of cows. Scatter points represent available data points from selected literature (refer to Table 2). Boxes (N > 5) are defined as the interquartile range (IQR) between 25th percentile (Q1) and 75th percentile (Q3) of the data according to the standard method. The black line in the box represents the median. Whiskers include data within 1.5 times of IQR below Q1 and above Q3. Plot generated with Python 3.10 using the Seaborn, Matplotlib, Numpy and Pandas libraries.

Biotransfer factors

Another frequently reported parameter is the biotransfer factor (BTF). Contrary to TF, and similarly to TR, it is not restricted to an exposure from a single source (e.g. feed) but can also account for contamination through multiple pathways such as air, wall paint chippings, soil, etc. BTF is a ratio of concentration in whole milk and the daily intake (dose) of a contaminant. Usually, the BTF is calculated on the basis of whole milk instead of milk fat, deviating from the standard for TF. Moreover, the BTF is not dimensionless and has units of time/mass, such as d/kg. In non-physiological approaches (see part II of this review^{(Reference Moenning, Krause, Lamp, Maul, Schenkel, Fürst, Pieper and Numata56)}) feed-to-milk BTFs are estimated on the basis of substance-specific octanol-water partition coefficients. However, BTF is more commonly applied in, for example, aquatic ecotoxicology (where the contaminant sources are multiple and diffuse) and is calculated as

(6) $${\it{BTF}}\left[ {{{\it{d}} \over {{\it{kg}}}}} \right] = {{{\it{ConcentrationInMilk}}\left[ {{{{\it{ng}}} \over {{\it{kg}}}}} \right]} \over {{\it{DailyIntake}}\left[ {{{{\it{ng}}} \over {\it{d}}}} \right]}}.$$

Again, this formula can be re-arranged, so that we can predict the concentration in milk given the daily intake and the BTF, that is,

(7) $$ConcentrationInMilk\left[ {{{ng} \over {kg}}} \right] = BTF\left[ {{d \over {kg}}} \right] \times DailyIntake\left[ {{{ng} \over d}} \right].$$

When feed is the only contamination source, the BTF in whole milk basis is related to TF in milk fat basis and TR by

(8) $${\it{TF}} = {\it{BTF}}\left[ {{{\it{d}} \over {{\it{kg}}}}} \right]/{\it{FatfractionInMilk}} \times {\it{feed}}\left[ {{{{\it{kg}}} \over {\it{d}}}} \right]$$

(9) $$\;{\it{TR}}\left[ {\it{\% }} \right] = {\it{BTF}}\left[ {{{\it{d}} \over {{\it{kg}}}}} \right] \times {\it{milk}}\left[ {{{{\it{kg}}} \over {\it{d}}}} \right] \times 100\% $$

In this review, we have opted not to transform BTF into TF or TR because of incomplete reported data (since this would have required assumptions). Data on BTFs are even more sparse than for TFs, and as shown in Fig. 3, BTFs are missing for most dl-PCBs. Rosenbaum et al. (2009)^{(Reference Rosenbaum, McKone and Jolliet130)} retroactively calculated BTFs on the basis of various studies^{(Reference Tuinstra, Vreman and Roos57,Reference Thomas, Sweetman and Jones65,Reference Thomas, Sweetman and Jones66,Reference Firestone, Clower and Borsetti76,Reference Heeschen and Blüthgen87,Reference McLachlan, Thoma and Reissinger109–Reference McLachlan111,Reference McLachlan and Richter113)} , which were also used in the depiction of BTFs in Fig. 3. Average congener-specific BTFs given in a report by the California Office of Environmental Health Hazard Assessment⁽¹⁵⁵⁾ were omitted, as these values were based on several experimental studies^{(Reference Slob, Olling and Derks62,Reference Schuler, Schmid and Schlatter63,Reference Thomas, Sweetman and Jones66,Reference Lorber, Fries and Winters67,Reference Thomas, Sweetman and Lohmann85,Reference Kerst, Waller and Reifenhäuser106,Reference McLachlan, Thoma and Reissinger109,Reference McLachlan and Richter113,Reference Huwe and Smith135)} . The data behind Fig. 3 can be found in Supplementary Materials Part A, Table S3. The coefficients of variation varied between 22% and 140%, which is in line with the previous observations for TFs and TRs. The reasons for this variability, also apparent in Fig. 3, may be similar to those presented above for TRs.

Fig. 3. Boxplots of biotransfer factors (BTFs) in whole milk basis for the milk of cows. Scatter points represent available data points from selected literature (refer to Table 2). Boxes (N > 5) are defined as the interquartile range (IQR) between 25th percentile (Q1) and 75th percentile (Q3) of the data according to the standard method. The black line in the box represents the median. Whiskers include data within 1.5 times of IQR below Q1 and above Q3. Plot generated with Python 3.10 using the Seaborn, Matplotlib, Numpy and Pandas libraries.

Fig. 4. Boxplots of α elimination half-lives. Scatter points represent available data points from selected literature (refer to Table 3) mostly for cows, with red dots indicating data from goats. Boxes (N > 5) are defined as the interquartile range (IQR) between 25th percentile (Q1) and 75th percentile (Q3) of the data according to the standard method. The black line in the box represents the median. Whiskers include data within 1.5 times of IQR below Q1 and above Q3. Plot generated with Python 3.10 using the Seaborn, Matplotlib, Numpy and Pandas libraries.

Fig. 5. Boxplots of β elimination half-lives. Scatter points represent available data points from selected literature (refer to Table 3), mostly for cows with red dots indicating data from goats. Boxes (N > 5) are defined as the interquartile range (IQR) between 25th percentile (Q1) and 75th percentile (Q3) of the data according to the standard method. The black line in the box represents the median. Whiskers include data within 1.5 times of IQR below Q1 and above Q3. Plot generated with Python 3.10 using the Seaborn, Matplotlib, Numpy and Pandas libraries.

Lastly, an appeal is presented to the next generation of risk assessors and risk managers: resist the temptation to use TF, TR and BTF as simple multiplicative factors to translate between maximum levels in food and maximum levels of contaminants in contaminated source materials (e.g. feed). Opt instead for full toxicokinetic models that capture the dynamics, physiological and metabolic variables explicitly (see part II of this review^{(Reference Moenning, Krause, Lamp, Maul, Schenkel, Fürst, Pieper and Numata56)}). For all their disadvantages, these transfer parameters nevertheless remain a good way of comparing studies, as we do in Figs. 1–3.

Elimination half-lives

Knowledge of the elimination half-lives or rate constants is of utmost importance for risk analysis. Elimination half-lives are necessary to estimate depuration time needed to reach concentrations in milk which are compliant with regulatory requirements after a contaminant exposure incident in a herd. At this point, an explanation is provided as to why multiple elimination half-lives are observed when the dairy cows are allowed a depuration phase after a period of exposure to lipophilic contaminants. The faster α elimination half-life dominates initially because the contaminants are readily available from the blood to be transferred into milk. This is sometimes called ‘elimination phase’ or ‘initial phase’. Any further flow of lipophilic contaminants into milk has to come from contaminants remobilised from fat, which is a slow compartment and makes the terminal β elimination half-life slower (slower = long in time units). This is referred to as the ‘distribution phase’ or ‘terminal phase’. The term ‘distribution phase’ is not recommended for the β phase, since it coincides with the name given in the pharmacokinetics of medicinal compounds to the α phase. To better understand the physical origin of the α and β elimination half-lives, please refer to part II of this review on Toxicokinetic Predictive Models for Risk Assessment^{(Reference Moenning, Krause, Lamp, Maul, Schenkel, Fürst, Pieper and Numata56)}.

Unfortunately, most studies neglect inclusion of a depuration period, as animals were slaughtered directly after exposure to assess the distribution of contaminants into various body compartments. Even if a depuration phase was included in an experiment, derived kinetics were often limited to the later and slower β phase, as the time resolution was not sufficient to observe the initial fast elimination phase. Hence, kinetic parameters for fast α elimination phase are not available for most congeners.

α half-lives derived from the initial fast elimination phase are plotted in Fig. 4, with the statistical data listed in Supplementary Materials Part A, Table S4. However, the information on α half-lives is sparse at best. For the majority of the PCDD/F congeners, only two studies contained values for the α elimination phase^{(Reference Driesen, Lerch and Siegenthaler114,Reference Huwe and Smith135)} , whereas kinetics for 1,2,3,7,8-PeCDF, 1,2,3,4,7,8,9-HpCDF and several PCBs is still unknown. Furthermore, the only available data for 2,3,7,8-TCDF α half-lives, as well as an indication for PCB-101 (<4 d) in dairy animals, are derived from a study with four alpine goats^{(Reference Fournier, Rychen and Marchand148)}. Three congeners were studied in more detail, namely 2,3,7,8-TCDD, 1,2,3,7,8-PeCDD and 2,3,4,7,8-PeCDF^{(Reference Roos, Berende and Traag61,Reference Jones, Safe and Morcom79,Reference Ruoff91,Reference Driesen, Lerch and Siegenthaler114,Reference Tuinstra, Roos and Berende116,Reference Huwe and Smith135,Reference Fournier, Rychen and Marchand148)} . According to these data, half-lives range from less than 1 d for OCDF and up to 2 weeks for 1,2,3,4,6,7,8-HpCDD. Furthermore, the data show high variation, especially for 2,3,7,8-TCDD.

β half-lives are distinctly longer than α half-lives, ranging from days to several months, and are plotted in Fig. 5 on the basis of statistical data depicted in Supplementary Materials, Table S5. Again, data for several dl-PCBs, ndl-PCB-28 and 1,2,3,7,8-PeCDF are unavailable. Furthermore, data for OCDF, PCB-169, PCB-52 and PCB-101 are available from only one feeding^{(Reference Huwe and Smith135)} and one field study^{(Reference Tremolada, Guazzoni and Parolini108)}, respectively. Additional data for cows were derived from feeding studies^{(Reference Firestone, Clower and Borsetti76,Reference Huwe and Smith82,Reference Ewers88,Reference Ruoff91,Reference Driesen, Lerch and Siegenthaler114,Reference Huwe and Smith135)} , single exposure studies^{(Reference Jensen and Hummel77,Reference Olling, Derks and Berende102)} , exposure during the dry phase^{(Reference Roos, Berende and Traag61,Reference Ruoff91)} , field studies^{(Reference Derks, Berende and Olling104,Reference Tremolada, Guazzoni and Parolini108)} and incident studies^{(Reference Malisch35,Reference Brambilla, Fochi and Falce41)} and for alpine goats^{(Reference Fournier, Rychen and Marchand148)} as listed in Table 3. Half-lives from the study by Roos et al. (1991)^{(Reference Roos, Berende and Traag61)} were re-evaluated by Tuinstra et al. (1992)^{(Reference Tuinstra, Roos and Berende116)}, which were used for the present data analysis. Half-lives expressed in weeks^{(Reference Malisch35)} or months^{(Reference Klein, Drochner and Forschner100)} were converted to days by multiplying the value by 7 or 30, respectively.

The heterogeneity of the experimental conditions and methodical approaches under which the data have been derived could be one reason for the variability evident in Fig. 5. Furthermore, the high variance for PCB-180 could be explained with the inclusion of β half-lives from goats, which are shorter compared with half-lives derived from cows^{(Reference Klein, Drochner and Forschner100,Reference Tremolada, Guazzoni and Parolini108)} . Fournier et al. (2013) explained this with the small body fat compartment and the relatively high milk fat yield seen in goats compared with cows^{(Reference Fournier, Rychen and Marchand148)}.

Aside from congener-specific data, α half-lives for sum parameters such as Aroclor 1254^{(Reference Fries70,Reference Fries, Marrow and Gordon71,Reference Willett and Liu78,Reference Saschenbrecker, Funnell and Platonow96)} , ∑PCDDF/s, ∑dl-PCBs and ∑ndl-PCBs^{(Reference Fournier, Rychen and Marchand148)}, I-TEQ^{(Reference Roos, Berende and Traag61)}, WHO₁₉₉₈-PCDD/F-TEQ and WHO₁₉₉₈-dl-PCB-TEQ^{(Reference Huwe and Smith82,Reference Huwe and Smith135)} as well as WHO₂₀₀₅-PCDD/F-TEQ^{(Reference Hoogenboom, Zeilmaker and van Eijkeren42)} are available. Furthermore, β half-lives are available for Aroclor 1254^{(Reference Fries70,Reference Fries, Marrow and Gordon71,Reference Willett and Liu78,Reference Perry, Everson and Hendrix119,Reference Hendriks, Smítková and Huijbregts156)} , ∑PCDDF/s and ∑dl-PCBs^{(Reference Fournier, Rychen and Marchand148)} ∑ndl-PCBs^{(Reference Ewers88,Reference Fournier, Rychen and Marchand148)} , I-TEQ^{(Reference Malisch35,Reference Roos, Berende and Traag61,Reference Olling, Derks and Berende102,Reference Traag, Mengelers and Kan105)} , WHO₁₉₉₈-PCDD/F-TEQ and WHO₁₉₉₈-dl-PCB-TEQ^{(Reference Huwe and Smith82,Reference Huwe and Smith135)} as well as WHO₂₀₀₅-PCDD/F-TEQ^{(Reference Brambilla, Fochi and Falce41,Reference Hoogenboom, Zeilmaker and van Eijkeren42)} .

Proposing transfer parameters for the sum of congeners may be useful for practical applications, but as sum parameters (such as TEF) change, these models lose their relevance. Furthermore, congener profiles in foods of animal origin and feed can be used as forensic tools to identify contamination sources^{(Reference Hoogenboom, Malisch and van Leeuwen14)}. Congener-specific reporting is mandatory for risk analysis.

A number of transfer parameters for this substance class can be summarised, but they are rarely all reported in a single study. As a consequence, for the parameters mentioned, not many reliable data points are available. In addition, the parameters display high variability. Possible factors influencing transfer parameters in experimental studies are discussed in depth according to the cow’s metabolic state and the contaminant properties in the following chapters.

Factors stemming from the cow’s metabolic state

Especially in high-performance cows, the energy requirements cannot be completely covered by the feed intake during early lactation. The resulting metabolic state of negative energy balance (NEB) needs to be offset by mobilisation of body reserves (mainly body fat). The tides are turned approximately 80–120 d after calving into a metabolic state with positive energy balance (PEB) thanks to increased feed intake capacity, leading to growing body fat reserves during later lactation. Thus, the contribution of mobilised body fat to total milk fat production is proportionally higher during the NEB in early lactation. During the later lactational stages in PEB, mainly de novo fat synthesis from ruminally produced short chain fatty acids and (in part) dietary fatty acids are the main source of milk fat^{(Reference Thomas, Sweetman and Jones65,Reference Stoop, Bovenhuis and Heck158)} . Body fat mobilisation in NEB and weight gain in PEB are assumed to be of major importance when discussing the release and deposition of lipophilic contaminants in lactating ruminants^{(Reference Rychen, Jurjanz and Toussaint52,Reference Rychen, Jurjanz and Fournier54,Reference Rossi, Bertuzzi and Vitali123,Reference McLachlan159)} . On the one hand, during NEB lipophilic contaminants might be released from body fat depots and excreted via milk^{(Reference Thomas, Sweetman and Jones66,Reference Klein99)} . On the other hand, in PEB initial concentrations of contaminants in the body fat would be ‘diluted’ in the fat compartment, and the reservoir for storage of these compounds increases^{(Reference Thomas, Sweetman and Jones66)}. Furthermore, distribution kinetics between blood and body fat depend on the blood/fat surface area, which increases with growth of body fat. A greater blood/fat surface area supposedly increases the transfer coefficient for blood/fat diffusion of lipophilic contaminants and affects their elimination through milk^{(Reference McLachlan159)}. As a consequence, differentiation of lactational stages into ‘early’, ‘mid’ and ‘late’ should be made to understand possible differences in transfer rate and α or β elimination half-lives (Table 4).

Table 4. Overview of the lactational status defined by days in milk (DIM) of cows in selected studies at beginning to end of the exposure phase to discuss the influence on transfer parameters

* Calculated from month p.p. multiplied by 30 d.

However, the influence of the metabolic state on the transfer of PCDD/Fs and PCBs into milk is still ambiguous in view of several field observations and feeding studies. For example, no increase of PCDD/Fs in milk fat was observed during nutritionally induced body fat mobilisation^{(Reference Schulz, Wiesmüller and Appuhn94)}. Three studies^{(Reference Thomas, Sweetman and Jones66,Reference Willett and Liu78,Reference Ewers88)} analysed the influence of NEB and PEB on the transfer of PCBs to milk during lactation. Thomas et al. (1999) conducted a long-term mass balance study in which the cows transitioned metabolic phases and described higher TRs for several PCBs during NEB than in PEB phase^{(Reference Thomas, Sweetman and Jones66)}. Similarly, Willet and Liu (1982) observed that excretion of PCBs was elevated for cows early in lactation compared with cows late in lactation^{(Reference Willett and Liu78)}. In contrast, Ewers (1987) described divergent effects of the lactational stage on the transfer of ndl-PCBs at two different dosage levels^{(Reference Ewers88)}. At the lower dosage, the TRs of ndl-PCBs were lower in early lactation, while no effect from the lactation period was evident with a 67% higher dose of the same ndl-PCB mixture on TRs, TFs and elimination half-lives. On the basis of the current data, no conclusive statement can be made on the influence of the lactational stage.

Regarding milk elimination half-lives, Brambilla et al. (2008) observed very short β half-lives compared with other studies in a herd of dairy cows after PCDD/F contamination. The authors attributed these findings to the physiological status of the herd, with 25% of the cows likely being in a negative energetic balance^{(Reference Brambilla, Fochi and Falce41)}. Fries et al. (1972) observed a tendency towards lower mean first-order rate constants for Aroclor 1254 depuration (half-lives were greater) for cows in early lactation compared with mid- and late lactation (Table 4)^{(Reference Fries70)}. However, no statistics were analysed to underline this statement. Importantly, a follow-up feeding study with nine cows in different stages of lactation saw no consistent relation between the first-order rate constants and the rate of body weight change^{(Reference Fries, Marrow and Gordon71)}.

Depuration kinetics of PCBs were further studied under the influence of a thyroprotein supplement, which induced body fat mobilisation and increased milk and milk fat production. During the thyroprotein treatment, elimination of PCBs in milk fat increased compared with a control group^{(Reference Willett and Liu78)}. Furthermore, Farries (1990) studied the excretion of ndl-PCBs in fifty dairy cows fed according to their energetic needs. During the depuration phase, half of the cows were purposely undersupplied by 30%, resulting in weight loss and a decrease in milk yield. The resulting fat mobilisation also caused a slight initial increase of PCB content in milk fat, whereas the PCB milk fat content in the energetically balanced cows further decreased^{(Reference Farries59)}.

During the dry period before lactation, the most important elimination pathway for the excretion of PCDD/Fs and PCBs through milk is not available, leading to increased bioaccumulation in body fat of the pregnant cow and the foetus. Several feeding studies described the exposure of animals during the dry period and observed transfer and elimination kinetics in milk postpartum with different results. Several studies observed a rapid decline of PCBs and PCDD/Fs in colostrum^{(Reference Ewers88,Reference Schulz93,Reference Schulz, Wiesmüller and Appuhn94,Reference Klein, Drochner and Forschner100,Reference Tuinstra, Roos and Berende116)} , whereas Hirako (2008) described a decline for most PCDD/Fs congeners while the dl-PCB content in colostrum and milk lipids was nearly constant^{(Reference Hirako127)}. Klein et al. (1992) stated that high milk yields promoted fast depuration postpartum^{(Reference Klein, Drochner and Forschner100)}, and Tuinstra et al. (1992) postulated that the depuration kinetics for PCDD/Fs postpartum could be different later in lactation^{(Reference Tuinstra, Roos and Berende116)}. Furthermore, Ewers et al. (1987) estimated TRs on the basis of the accumulative input and output, and stated that these TRs were higher in early lactational state compared with TRs derived from cows later in lactation^{(Reference Ewers88)}. Differences in body fat dynamics, PCDD/F and dl-/ndl-PCB absorption or metabolic clearance rates may also be attributed to parity^{(Reference Driesen, Lerch and Siegenthaler114)}.

Body weight and body fat content

Aside from dynamic changes in body fat due to metabolism, the size of the fat compartments is an important factor for distribution and storage of lipophilic substances like PCDD/Fs and PCBs. However, quantification of fat compartments in a living animal is challenging. Hence, proxies, such as body weight or verbal description of the body fat content, are used to explain the influence of fat compartment sizes on transfer kinetics.

Fries et al. (1973) concluded that there was no connection between body weight of the cows and elimination half-lives in milk^{(Reference Fries, Marrow and Gordon71)}. In a follow-up study in 1999, the same authors observed no connection between transfer rates and body weight^{(Reference Fries, Paustenbach and Mather72)}, whereas by contrast, McLachlan and Richter (1998) suggested that TR could be influenced by body weight, albeit not strongly^{(Reference McLachlan and Richter113)}.

Ewers et al.^{(Reference Ewers88–Reference Ewers, Reichmuth and Wetzel90,Reference Ewers, Heeschen and Reichmuth131)} noted that TR increased with decreasing body fat percentage, though milk yields and dosage levels also need to be considered. On the basis of data from the feeding study published by Ewers et al.^{(Reference Ewers88,Reference Ewers and Heeschen89,Reference Ewers, Heeschen and Reichmuth131)} , Heeschen et al. (1993) stated that the biological half-lives of PCBs in cows with an elevated body fat percentage are twice as high compared with animals with an estimated low body fat percentage, regardless of milk fat yield^{(Reference Heeschen, Ewers and Blüthgen132)}.

In another study, shorter half-lives for a cow with low body fat compared with three other cows were calculated. The authors postulated that with a lower body fat weight the volume for distribution of lipophilic substances would also be small, and thus the transfer from body fat to milk would be elevated^{(Reference Roos, Berende and Traag61,Reference Tuinstra, Roos and Berende116)} . A similar observation regarding the decline of PCB concentrations in milk of two cows with different body weights was described by Klein (1991)^{(Reference Klein99)}.

Milk and milk fat production

The lipid content of milk depends on the lactation stage, individual animal, breed and feed composition. Furthermore, the daily milk fat production is related to the daily milk yield and the metabolic state of the cow. Both of these performance parameters have been steadily increased through breeding measures for dairy cows⁽¹⁶⁰⁾, a trend that also reflects in data of the cited transfer studies (Table 5).

Table 5. Milk and milk fat production of selected studies in chronological order

* Calculated from L/d by multiplying with a density of 1.04 kg/l and rounded to one decimal place.

Owing to the lipophilic nature of PCDD/Fs and PCBs, these compounds have a high affinity to milk lipids. It is therefore reasonable to assume that the daily milk fat yield has an effect on the transfer parameters. McLachlan et al. (1998) stated that the interaction of PCDD/F TRs and lactation rate, among other factors, is not very pronounced^{(Reference McLachlan and Richter113)}. Other studies mentioned a positive correlation between milk yield^{(Reference Fries, Paustenbach and Mather72,Reference Huwe and Smith135)} or milk fat yield^{(Reference Fries, Paustenbach and Luksemburg73)} and the TRs of PCDD/Fs into milk. TFs of PCDDF/s were found to be less dependent on milk production, but could be related to milk fat content^{(Reference Fries, Paustenbach and Mather72)}.

In a feeding study, total recovery of ndl-PCBs (PCB-138, PCB-153 and PCB-180) in milk was higher in a cow with higher milk yield but also lower body fat percentage compared with another cow with less milk yield and higher body fat percentage^{(Reference Heeschen and Blüthgen87)}. In a follow-up experiment with three cows, TRs increased with higher milk yields and lower body fat percentages^{(Reference Ewers88,Reference Ewers and Heeschen89,Reference Ewers, Heeschen and Reichmuth131,Reference Heeschen, Ewers and Blüthgen132)} . These observations coincide with a recent feeding study with primiparous (lower milk fat yield, lower body weight) and multiparous (higher milk fat yield, higher body weight) cows, suggesting that lower milk fat yields could be one reason for lower PCDD/F and dl-/ndl-PCB TRs^{(Reference Driesen, Lerch and Siegenthaler114)}.

The influence of milk fat and milk yield on the depuration phase was discussed in several studies. Klein et al. (1992) stated that differences in milk fat production at comparable milk yields did not clearly correlate with PCB excretion rates^{(Reference Klein, Drochner and Forschner100)}. Heeschen et al. (1993) assumed that the mean milk fat production had no effect^{(Reference Heeschen, Ewers and Blüthgen132)}, and Rossi et al. (2010) concluded that averaged daily milk yields per cow did not affect the rate of PCB excretion^{(Reference Rossi, Bertuzzi and Vitali123)}. Furthermore, Fries et al. (1973) found no significant correlation between milk fat production per cow and PCB excretion rates^{(Reference Fries, Marrow and Gordon71)}.

However, Klein et al. (1992) also stated that elimination half-lives from cows, with comparable body weight and PCB milk fat content before depuration, were shorter with higher milk yields^{(Reference Klein, Drochner and Forschner100)}. Recently, Driesen et al. (2022) derived overall shorter PCB elimination half-lives for multiparous cows with higher milk fat yields compared with primiparous cows. However, a dependency between elimination half-lives calculated for PCDD/Fs and milk fat yield was less conclusive, with half-lives for PCDD/F TEQs being even shorter for primiparous cows^{(Reference Driesen, Lerch and Siegenthaler114)}.

Overall, variation of milk fat production within singular studies could be too small to observe significant effects on transfer parameters. Therefore, the influence of this factor cannot be assessed conclusively on the basis of the current data situation.

A final potentially important factor is the milk fat origin in terms of its proportion from de novo synthesised milk fat to remobilised body fat, as discussed above in the section on metabolic state.

Animal health

Little is known on whether animal health status has an impact on the transfer of PCDD/Fs and PCBs into milk. Absorption of PCBs in the gastrointestinal tract might be restricted by diarrhoea as the passage of feed is accelerated. Hence, the transfer into milk and tissue fat is reduced compared with healthy cows^{(Reference Furr, Mertens and Gutenmann74)}. Mastitis, on the other hand, might promote the transfer of PCDD/Fs into the milk, as the mammary gland blood–milk barrier becomes more permeable^{(Reference Fries, Paustenbach and Mather72)}. Field studies and incident investigations on PCDD/F and PCB contamination rarely mention the animal health status. Thus, a possible influence on the results of the studies included in this review can neither be excluded nor confirmed.

In turn, PCDD/F and dl-PCB exposure may have effects on animal health, such as oxidative damage combined with reduced levels of antioxidants in the blood^{(Reference Spagnuolo, Sarubbi and Rossetti161,Reference Spagnuolo, Cigliano and Nebbia162)} and chromosome fragility^{(Reference Genualdo, Perucatti and Iannuzzi163)}. Exposure to elevated levels of TCDD was even suspected to be the cause of abortions and stillbirths in cattle^{(Reference Davies, Desouza and Waghela164)}. However, the European Food Safety Authority (EFSA) concluded that all the above-mentioned studies are not fit for risk assessment owing to missing data or exposure to other potential hazardous substances^{(Reference Knutsen, Alexander and Barregård16)}.

No adverse effects on the conversion of feed to milk or body weight were observed in dairy cattle exposed to high amounts of 1 g/d (1.7 mg/kg body weight/d^{(Reference Willett, Liu and Fries121)}) Aroclor 1254^{(Reference Willett, Liu and Durst81)} over several days. Furthermore, during in vitro fermentation experiments on micro-organism activity from the rumen of Holstein steers, no effect in terms of dry matter disappearance was observed in presence of different PCB concentrations. The toxic effects of ndl-PCBs on laboratory animals are generally difficult to identify, since they rarely appear in a pure form. Even less information is available on specific effects of ndl-PCBs on the health status of dairy cows. It can be expected that very high doses of pure ndl-PCB 180 might have neurotoxic, endocrine and behavioural effects^{(Reference Viluksela, Heikkinen and van der Ven165)}, but such doses are not expected in practice for farm animals.

Factors stemming from the contaminants

Contaminant source-dependent bioavailability

Ruminants may be exposed to PCDD/Fs and PCBs via various sources, such as roughage, concentrates and mineral supplements, but also wall paints and wood preservatives, soil, sewage sludge and fly ash. The bioavailability of the contaminants from different sources (matrices, Table 6), that is, primarily the absorption of PCDD/Fs and PCBs by the ruminants, needs to be considered when discussing transfer parameters. Furthermore, PCBs have also been administered intravenously^{(Reference Safe, Platonow and Hutzinger120)} as well as intramuscularly^{(Reference Vrecl, Ursic and Pogacnik125)} to sheep.

Table 6. Transfer studies grouped according to the contamination source

On the basis of two successive mass balance studies, McLachlan et al. (1998) concluded that digestive tract absorption of PCDD/Fs from sewage sludge, as the contamination source for grass silage, was comparable to grass silage contaminated by atmospheric deposition^{(Reference McLachlan and Richter113)}. The bioavailability of PCDD/Fs in fly ash collected from a filter of a municipal solid waste incinerator and administered intra-ruminally was lower than the value estimated for grass from a pasture near a large municipal solid waste incinerator^{(Reference Slob, Olling and Derks62)}. TFs of PCDD/Fs derived from a feeding study with pentachlorophenol-treated wood^{(Reference Fries, Paustenbach and Mather72)} compared with a study with contaminated magnesium mineral supplement suggested a reduced bioavailability of PCDD/Fs from pentachlorophenol-treated wood^{(Reference Huwe and Smith82)}.

Fürst et al. (1993) stated that soil intake of grazing cows is either less than assumed or that the bioavailability of PCDD/Fs from soil is lower than expected^{(Reference Fürst, Krause and Hein122)}. A lower bioavailability of several PCDD/Fs and PCBs from soil was also hypothesised by Driesen et al. (2022), who calculated tendentially higher TRs for a control group fed grass-silage compared with another group fed grass-silage mixed with contaminated soil^{(Reference Driesen, Lerch and Siegenthaler114)}. However, according to several studies with goats and cows, bioavailability of PCBs from soil is comparable to maize silage^{(Reference Fournier, Rychen and Marchand148)}, hay^{(Reference Ounnas, Feidt and Toussaint146)} or grain^{(Reference Jones, Safe and Morcom80)}. Compared with a spiked oil matrix, the bioavailability of PCBs from a sandy soil contaminated by a fire is slightly reduced^{(Reference Feidt, Ounnas and Julien-David147)}.

The absorption of PCDD/Fs from contaminated lime, when used as a neutraliser for acidic citrus pulp, is comparable to that of grass. During this process, the lime is completely dissolved and the PCDD/Fs are absorbed in the citrus pulp^{(Reference Malisch35)}. Slob et al. (1995) hypothesised increased lipid content and smaller particle sizes as factors that could increase bioavailability^{(Reference Slob, Olling and Derks62)}. An overview of various TFs according to different matrices as carriers for the contamination is given by Huwe et al. (2005)^{(Reference Huwe and Smith135)}, in which the substantially lower TFs for fly ash and grass contaminated with municipal solid waste^{(Reference Slob, Olling and Derks62)} are attributed to particulate binding^{(Reference Huwe and Smith135)}. Furthermore, bioavailability of PCDD/Fs and PCBs also depends on the physico-chemical properties of the congeners, such as degree of chlorination.

Degree of chlorination and partition coefficients

The octanol-water partition coefficient ${K_{{\rm{ow}}}}$ , as well as its logarithm ${\rm{log}}\;\left( {{K_{{\rm{ow}}}}} \right)$ , are widely used measures of the lipophilicity of a compound. ${K_{{\rm{ow}}}}$ correlates with the in vivo thermodynamic equilibrium partition (or preference) of the congeners among water-rich and fat-rich tissues. In turn, the number of chlorine atoms in PCDD/F and PCB congeners correlates with ${K_{{\rm{ow}}}}$ . With more chlorine atoms in a congener, its ${K_{{\rm{ow}}}}$ increases, and its solubility preference for the aqueous phase decreases^{(Reference Shiu, Doucette and Gobas168)}. All PCDD/Fs and PCBs have a ${K_{{\rm{ow}}}}$ larger than 1, which implies that these substances are lipophilic and hydrophobic, hence their distribution into body and milk fat. ${K_{{\rm{ow}}}}\;$ values are used to predict toxicokinetic model parameters, since they correlate with the preference of contaminants for different compartments, such as blood, tissues and udder fat in the cow. The TRs^{(Reference Welsch-Pausch and McLachlan64,Reference Lorenzi, Angelone and Ferretti86,Reference McLachlan, Thoma and Reissinger109,Reference McLachlan and Richter113,Reference Costera, Feidt and Marchand144,Reference McLachlan169)} , BTFs^{(Reference Fries, Paustenbach and Mather72)} and bioavailability^{(Reference Olling, Derks and Berende102)} of 2,3,7,8-substituted PCDD/Fs, with the exception of 2,3,7,8-TCDF and 1,2,3,7,8-PCDF, decrease with the number of chlorine substituents. This behaviour can be attributed to a decrease in absorption over the gastrointestinal wall^{(Reference Tremolada, Guazzoni and Parolini108,Reference Jilg, Mueller and Hagenmaier112)} , due to the increased hydrophobicity of highly chlorinated PCDD/Fs^{(Reference Schuler, Schmid and Schlatter63)}.

Furthermore, lower chlorinated PCDD/Fs migrate principally into the lipoprotein fraction of blood, while higher chlorinated PCDD/Fs bind more readily to other blood plasma proteins with a prolonged dwell time in systemic circulation^{(Reference Hirako127,Reference Patterson, Fürst and Henderson170)} . In addition, the extent of deposition of PCDD/Fs into subcutaneous fat was found to be inversely correlated to the degree of chlorination. This could explain the faster elimination rates of heptachlorinated PCDD/Fs compared with lower-chlorinated congeners^{(Reference Olling, Derks and Berende102)}. A faster distribution of lower-chlorinated PCDD/Fs from liver to fat tissues was also observed by Richter and McLachlan (2001), who found that higher-chlorinated PCDD/Fs were accumulated in the liver^{(Reference Richter and McLachlan171)}, likely owing to the low affinity of these compounds to lipoproteins^{(Reference Hirako127,Reference McLachlan169)} . Other studies found no correlations between elimination half-lives and the number of chlorine substituents^{(Reference Tuinstra, Roos and Berende116,Reference Huwe and Smith135)} .

Fluxes of PCBs in faeces of cows showed that absorption in the gastrointestinal tract also decreased with increasing degree of chlorination^{(Reference McLachlan111)}. Furthermore, the residence times of PCBs in the cow, calculated as body burden divided by amount absorbed, increased with the degree of chlorination^{(Reference Thomas, Sweetman and Jones65)}. TRs derived in studies with lactating alpine goats also showed no clear correlation^{(Reference Ounnas, Feidt and Toussaint146)} or indications for an inverse correlation with degree of chlorination^{(Reference Costera, Feidt and Marchand144)}. However, aside from the degree of chlorination and ${K_{{\rm{ow}}}}$ , the substitution pattern, and thus metabolic stability, also needs to be considered when discussing effects on transfer parameters.

Metabolism of PCDD/Fs and PCBs

PCDD/Fs and PCBs are known to be persistent in the environment, with half-lives of up to several decades^{(Reference Sinkkonen and Paasivirta12)}. However, some congeners are more readily enzymatically metabolised by the cow, with the velocity and extent being largely dependent on the chlorination pattern. One metabolic pathway is the typical phase-1 metabolism, that is, hydroxylation of PCBs in the animal and subsequent excretion via urine and, to a lesser extent, via milk^{(Reference Gardner, Righter and Roach75,Reference Safe, Platonow and Hutzinger120)} .

However, metabolism of PCBs is congener specific. One hypothesis is that PCBs with a 4,4′-substitution pattern and, to a lesser degree, with a 2,3,5-substitution pattern, are less likely to be metabolised^{(Reference McLachlan111)}. Another hypothesis is that PCBs with adjacent meta- and para-hydrogen atoms are more chemically stable, and PCBs with adjacent ortho- and meta-hydrogen atoms are only partially metabolised^{(Reference Thomas, Sweetman and Jones65)}. However, both hypotheses have their shortcomings, as there are PCBs that do not behave as predicted by these rules in terms of substitution patterns^{(Reference Thomas, Sweetman and Jones65)}.

PCDD/F congeners with a 2,3,7,8-chlorine substitution pattern are largely metabolically stable and are transferred into the milk in unchanged form, while other congeners can be metabolised in the animal^{(Reference Welsch-Pausch and McLachlan64,Reference Firestone, Clower and Borsetti76,Reference McLachlan, Thoma and Reissinger109)} . Additionally, 2,3,7,8-substituted furan congeners without chlorine at 4 and 6 position^{(Reference Fries, Paustenbach and Mather72,Reference Fries, Paustenbach and Luksemburg73)} , such as 2,3,7,8-TCDF^{(Reference Olling, Derks and Berende102)} and 1,2,3,7,8-PeCDF^{(Reference McLachlan and Richter113)}, are susceptible to metabolism. In vitro fermentation experiments showed no significant metabolic degradation of PCBs^{(Reference Willett, Loerch and Willett172)} or PCDD/Fs^{(Reference Fries, Paustenbach and Luksemburg73)} by rumen microorganisms.

The transfer parameters for dioxins and PCBs in experimental studies can be influenced by several factors, some attributable to the cow physiology and others related to the chemical contaminant. There are some contradictions in the literature regarding the assessment of the influence of the factors responsible for the variability in transfer parameters, often aggravated by incomplete information. Comparability is not trivial, since there are many different possible study designs, due to many variables, such as animal species and breed, milk yield, type of congener administered and duration of supplementation. On the basis of existing gaps in the data and the multitude of different studies, the literature data are not always applicable and comparable to produce predictive models. However, several toxicokinetic predictive models have been proposed and successfully parametrised in the literature, and shown to agree well at least with the specific experimental data they are based on.